Graph pattern recognition interface

ABSTRACT

In some example embodiments, a system and method are illustrated as including receive pattern data that includes transaction data relating to transactions between persons. Next, the system and method may include building at least one secondary network based upon the pattern data. Additionally, the system and method may include displaying the at least one secondary network.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/963,452 filed Dec. 21, 2007, now U.S. Pat. No. 8,046,324, entitled,“GRAPH PATTERN RECOGNITION INTERFACE,” which claims priority under 35U.S.C. §119(e) to U.S. Provisional Patent Application entitled “GRAPHPATTERN RECOGNITION INTERFACE,” (Ser. No. 60/991,539) filed on Nov. 30,2007, which applications are incorporated by reference in their entiretyherein.

TECHNICAL FIELD

The present application relates generally to the technical field ofalgorithms and programming and, in one specific example, the displayingof transaction data and patterns developed therefrom.

BACKGROUND

Social networks define certain characteristics regarding persons interms of habit, values, and the like. In certain cases, patterns may bedetected within social networks, where these patterns reflect habits,values, and the like. For example, if one member of a social networkengages in certain behaviors, then it may be implied that other membersof the social network also may engage in these behaviors.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 is a diagram of a system, according to an example embodiment,illustrating the generation and display of pattern data.

FIG. 2 is a diagram of a Graphical User Interface (GUI), according to anexample embodiment, displaying the pattern data (e.g., a primarynetwork) and various secondary networks here displayed in a referencematrix.

FIG. 3 is a GUI, according to an example embodiment, displaying aprimary network and, additionally, various secondary networks whereinthis primary network relates, more generally, to purchaser networks.

FIG. 4 is a diagram of a GUI, according to an example embodiment,containing popup information relating to a particular selected secondarynetwork.

FIG. 5 is a block diagram of a computer system, according to an exampleembodiment, used to generate a reference matrix as it may appear withina GUI.

FIG. 6 is a dual stream flowchart illustrating a method, according to anexample embodiment, used to generate a reference matrix as it may appearwithin a GUI.

FIG. 7 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation that selects an ArtificialIntelligence (A.I.) algorithm, or combinations of A.I. algorithms.

FIG. 8 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation that parses pattern data andextracts certain characteristics relating to the pattern data so as togenerate a pattern characteristic set.

FIG. 9 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation that retrieves the A.I.algorithms and executes an A.I. engine.

FIG. 10 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation that generates a matrix such asthe reference matrix using the A.I. generated dataset.

FIG. 11 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation that builds an A.I. datastructure (e.g., a genetically derived tree) using the A.I. algorithmretrieved from the A.I. algorithm database.

FIG. 12 is a diagram of graphs, according to an example embodiment, thatmay be generated as a result of the execution of a mutation functionthat combines the selected historical data and pattern characteristicset data.

FIG. 13 is a diagram of a plurality of graphs, according to an exampleembodiment, that are generated through the execution of a crossoverfunction.

FIG. 14 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation that builds an A.I. datastructure (e.g., a neural network) using an A.I. algorithm retrievedfrom the A.I. algorithm database.

FIG. 15 is a diagram of a neural network, according to an exampleembodiment, generated through the application of a learning algorithm.

FIG. 16 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation that when executed selectscertain patterns.

FIG. 17 is a flowchart illustrating a method, according to an exampleembodiment, used to execute an operation to transmit for storage theselected pattern(s) into a manual pattern database.

FIG. 18 is a Relational Data Schema (RDS), according to an exampleembodiment.

FIG. 19 shows a diagrammatic representation of a machine in the exampleform of a computer system, according to an example embodiment.

DETAILED DESCRIPTION

A system and method follows for displaying a graph and patterns relatedto the graph. In the following description, for purposes of explanation,numerous specific details are set forth to provide a thoroughunderstanding of some embodiments. It may be evident, however, to oneskilled in the art that some embodiments may be practiced without thesespecific details.

In some example embodiments, a system and method is illustrated thatallows for a specific graph to be identified via the graphs similarityto a recognized pattern. A recognized pattern may be generated through amanually recognized pattern, or algorithmically. In some example cases,a GUI is implemented that contains a primary network and at least onesecondary network, where both the primary and secondary networks aregraphs. In some example cases, this primary network may be a suspectedfraud network, a purchaser's network, or some other network depictingrelationships between persons. This primary network may also reflectrepresentations of identity in a graphical format. These representationsmay include email addresses and other suitable information forrepresenting a person. Additionally, in some example embodiments, the atleast one secondary network may be displayed in a reference matrix, areference list, or through some other suitable way of displaying apattern in a GUI. A user such as a fraud prevention specialist,marketing professional, customer service representative, or othersuitable person may then analyze the primary network and compare it tothe at least one secondary network. Where a match is determined to existbetween the primary network and the secondary network, the user canmanually select this match. By manually selecting a match, the primarynetwork may be classified as a type related to the secondary network.For example, if the secondary network is associated with aclassification “B”, then the primary network may also be associated withthis classification “B”. Once classified, the primary network may bestored for future use as a secondary network. This process of storingfor future use may be classified as feedback. Classifications, ingeneral, may be associated with types of fraud schemes, marketingnetworks, or some other suitable type of network.

In some example embodiments, secondary networks may be generated throughanalyzing a primary network. In one example embodiment, a primarynetwork is analyzed for its properties, and these properties are thenused to determine similar secondary networks. As will be more fullydiscussed below, this determination may be carried out through the useof A.I. algorithms, or through some type of mapping between nodes andedges of the primary network and nodes and edges of a secondary network.These A.I. algorithms may also include certain statistical techniques,or advanced statistical techniques. Further, these statisticaltechniques, or advanced statistical techniques, may be used in lieu ofan A.I. algorithm in some example cases.

Some example embodiments may include the use of transaction data takenfrom various on-line transactions to generate both a primary andsecondary network. For example, in a transaction between two persons(e.g., a natural or legal person such as a corporation), an account heldby a person may form the nodes of the network, and the actualtransactions extending between the accounts may form edges in thenetwork. In some example embodiments, email addresses, cookies, generalmachine identification (e.g., a Media Access Control Address (MAC)), orother suitable identification may be used to distinguish persons. Thenetwork may be the previously referenced primary and/or secondarynetwork.

Example System

FIG. 1 is a diagram of an example system 100 illustrating the generationand display of pattern data. Shown is a user 101 who, utilizing any oneof a number of devices 102, may generate a pattern request 107. This anyone of a number of devices 102 may include for example a cell phone 103,a computer 104, a television 105 and/or a Personal Digital Assistant(PDA) 106. Residing on any one of the number of devices 102 may be, forexample, a Graphical User Interface (GUI) 116. Utilizing this GUI 116,the user 101 may generate the pattern request 107. The pattern request107 may be a Hyper Text Transfer Protocol (HTTP) based query thatutilizes other technologies including a Hyper Text Markup Language(HTML), an eXtensible Markup Language (XML), Dynamic-HTML (DHTML),Asynchronous JavaScript and XML (AJAX), JavaScript, Applets, or someother suitable technology. Further, a Structured Query Language (SQL),or a Multidimensional Expression Language (MDX) may also be used, inpart, to generate the pattern request 107. This pattern request 107 maybe transmitted, in some example embodiments, across a network (notpictured) to a database server 108. This network may be an Internet,Wide Area Network (WAN), a Local Area Network (LAN), or some othersuitable network. This database server 108 may be operatively connectedto, for example, a relational database 109 and/or an Online AnalyticProcessing (OLAP) database 110. Upon receiving the pattern request 107,the database server 108 may retrieve from either or both the relationaldatabase 109 and OLAP database 110 pattern data 111. This pattern data111 may be then sent across a network (not pictured) to be displayed inthe GUI 116.

In some example embodiments, the pattern data 111 is a formatted filecontaining data describing a graph. This data may include node types andnames, numbers of edges, numbers of degrees per node, types and numberof edges connecting nodes, and other suitable information. In someexample embodiments, the formatted file is formatted using XML, sometype of character delimitation (e.g., a semi-colon delimited flat file,or comma delimited flat file), or some other suitable method offormatting. Some example embodiments may include the pattern data 111being a Joint Photographic Experts Group (JPEG) formatted image.

Once displayed, the user 101 may select a portion of the pattern data111 for further details. In some example embodiments, the pattern data111 is transmitted by the database server 108 not only to be displayedin the GUI 116, but also the database server 108 may transmit thispattern data 111 to a pattern server 112. Upon receiving the patterndata 111, the pattern server 112 may determine patterns that are similarto the pattern data provided in the pattern data 111. This determinationmay be based upon various Artificial Intelligence (A.I.) algorithmsstored in an A.I. algorithm database 113. Further, pattern server 112may utilize a manual pattern database 114 containing various types ofpatterns manually stored or, in some cases, automatically stored by, forexample, a user 101. These various patterns closely correspond to, forexample, the pattern contained within the pattern data 111. Further, insome cases, historical data stored in a historical data store 120 may beaccessed by the pattern server 112 so as to be utilized to, for example,train an A.I. algorithm contained within the A.I. algorithm database113.

In one example embodiment, the pattern data 111 is received by thepattern server 112, processed, and a plurality of secondary network data115 (e.g., patterns) retrieved. This secondary network data 115 may bedisplayed in the GUI 116 along with the pattern data 111. The patterndata 111 may be displayed as a primary network 220. In some cases, theuser 101 may, for example, select one or more of the secondary networkdata 115. This selection may include the utilization of some type ofsuitable input device such as a mouse, keyboard, light pen, touchscreen, or other suitable device so as to allow the user 101 to selectone of these secondary networks. Once selected, the pattern data 111 maybe categorized as a part of a particular taxonomy and then transmittedas a selected pattern or patterns 130 from the one or more devices 102to the pattern server 112. This transmission of the selected pattern orpatterns 130 may take place over, for example, a network (not shown)such as an Internet, LAN, or WAN. Once the pattern server 112 receivesthe selected pattern or patterns 130, it may then store the selectedpatterns into, for example, the manual pattern database 114 and/or thehistorical data store 120.

Example Interface

FIG. 2 is a diagram of an example GUI 116 displaying the pattern data111 (here referenced as a primary network) and various secondarynetworks here displayed in a reference matrix 210. Shown is a primarynetwork 220, such as for example, a suspected fraud network. Containedwithin this primary network 220 is a graph composed of a plurality ofnodes and edges. For example, a node 201 is connected to a node 202 viaan edge 206. This node 202, in turn, is connected to a node 205 via anedge 208. Further, the node 202 is connected to a node 204 via an edge209. Additionally, the node 202 is connected to a node 203 via an edge207. In some example embodiments, each one of these nodes represents anaccount whereas each one of these edges (e.g., 206 through 209)represents a relationship in the form of a transaction between accounts.This transaction between accounts (e.g., transaction data) may be asales transaction, refund transaction, a payment transaction, or othersuitable transaction. In some example embodiments, the edges aredirected (e.g., in cases where funds are flowing from one account toanother), while in other cases the edges are bi-directional. In someexample embodiments, the user 101, utilizing some type of I/O device,may select one or more patterns (e.g., graphs) displayed within thereference matrix 210. For example, shown is a row 211 containing one ormore patterns. Further, a row 212 also shows various patterns, and a row213 further shows various patterns. Further, as illustrated herein, amouse pointer 214 is used to select the first secondary networkdisplayed in the row 211 as being similar to the primary network 220.

FIG. 3 is an example GUI 116 displaying a primary network and,additionally, various secondary networks wherein this primary networkrelates, more generally, to purchaser networks. Shown is a GUI 116containing a number of graphs. These graphs represent, for example,various networks. For example, primary network 301 contains a networkcomposed of nodes and edges. Node 302 is shown as are nodes 303, 304 and305. The node 302 is connected to the node 305 via an edge 308. The node302 is connected to the node 304 via an edge 309, and the node 302 isfurther connected to a node 303 via an edge 307. Further shown is areference matrix 310 containing a plurality of secondary networks. Thesesecondary networks may be provided through various secondary networkdata 115 generated and transmitted by the pattern server 112 and thendisplayed on or as part of the GUI 116. In some example embodiments, theuser 101, utilizing a mouse pointer 314, may select, for example, agraph or network appearing within the row 311. Additional rows shown inthis reference matrix 310 is a row 312 and a row 313, each of whichcontains a plurality of networks. In some example embodiments, purchasernetwork, such as primary network 301, may refer to various purchasers ofparticular goods or services as denoted by nodes 302, 303, 304 and 305and relationships between these various purchasers in the form of edges307, 308 and 309. As discussed elsewhere, these edges may be directed ornon-directed, bi-lateral edges.

FIG. 4 is a diagram of an example of GUI 116 containing popupinformation relating to a particular selected secondary network. Shownis a popup 401 containing the description of a secondary network. Thispopup 401, in some example embodiments, may be generated through themouseover function or other suitable function engaged in by the user 101wherein this user 101 may, for example, perform mouseover of, forexample, a graph appearing in, for example, row 312. This mouseover maybe facilitated through the use of, for example, mouse pointer 402 toselect the pattern and thereby generate the mouseover function inoperation. Once the mouseover function operation is executed, then thepopup 401 will be displayed. This popup 401 may contain detailedinformation relating to the particular graph to which the mouse pointer402 is applied for purposes of a mouseover operation or function.

In some example embodiments, the GUI 116 is implemented using anyone ofa number of technologies. These technologies may include a browserapplication, or a stand alone application. This browser application maybe capable of interpreting HTML, XML, or some other suitable markuplanguage. As to a stand alone application, in some example embodiments,a programming language such as C#, Java, C++, or others may be used togenerate a stand-alone application.

Example Logic

FIG. 5 is a block diagram of a computer system 500. This computer system500 may be, for example, a pattern server 112, or one of the devices102. The various blocks illustrated herein may be implemented insoftware, firmware, or hardware. Shown is a receiver 501 to receivepattern data that includes transaction data relating to transactionsbetween persons. A building engine 502 is also shown to build at leastone secondary network based upon the pattern data. A display 503 isshown to display the at least one secondary network. In some exampleembodiments, the building engine 502 may build at least one secondarynetwork with an A.I. algorithm that processes at least one of a patterncharacteristic set, or historical data. The display may include aplurality of secondary networks (see e.g., GUI 116). Some exampleembodiments may include, the building engine 502 including at least onesecondary network built by mapping manual patterns to the pattern data.A storage engine 504 may be implemented to store the at least onesecondary network into a data store as a manual pattern. The manualpattern includes a pattern selected by a user. A request engine 505 maybe implemented to process a request for a quick reference matrix basedupon the pattern data. In some example embodiments, the secondarynetwork includes at least one of node, or edge data generated through ananalysis of pattern data. Further, the transaction data may include atleast one of a sales transaction data, or a payment transaction data.

FIG. 6 is a dual stream flowchart illustrating an example method 600used to generate a reference matrix as it may appear within, forexample, a GUI 116. Illustrated is a first stream containing a pluralityof operations 601 through 603, and operations 614 through 617. Alsoshown is a second stream containing operations 604 through 613. Withregard to the first stream, in some example embodiments, an operation601 is executive that retrieves pattern data. This pattern data may be,for example, the pattern data 111. Upon execution of the operation 601,a further operation 602 may be executed that requests a quick referencematrix based upon the pattern data. This quick reference matrix may be,for example, the reference matrix 310. An operation 603 may then beexecuted that transmits the pattern data 111 across a network to bereceived through the execution of an operation 604.

In some example embodiments, a decisional operation 605 may be executedthat determines whether or not various A.I. algorithms may be utilizedin the generation of the various secondary networks contained within thereference matrix such as reference matrix 310. In cases where decisionaloperation 605 evaluates to “true,” an operation 606 may be executed thatselects an A.I. algorithm or combinations of A.I. algorithms. Anoperation 608 may be executed that parses the pattern data, which ispattern data 111, and extracts certain characteristics relating to thepattern data so as to generate a pattern characteristic set. Anoperation 612 may be executed that retrieves the A.I. algorithms andexecutes an A.I. engine wherein these A.I. algorithms are retrievedfrom, for example, an A.I. algorithm database 113. An operation 613 maybe executed that generates a matrix such as the reference matrix 310using the A.I. generated dataset. An operation 611 may then be executedthat transmits the matrix as secondary network data 115. In cases wheredecisional operation 605 evaluates to “false,” an operation 607 may beexecuted. This operation 607 may map the pattern data to patternscontained within the manual pattern database 114. An operation 609 maybe executed that retrieves patterns from the manual pattern database 114for the purposes of executing operation 607. An operation 610 may beexecuted that generates a matrix using the retrieved patterns. Thismatrix may be, for example, referenced the reference matrix 310. Anoperation 611 may then be executed to transmit the matrix. Once thesecondary network data 115 is transmitted, an operation 614 may beexecuted that receives the secondary network data. An operation 615 maybe executed that displays the matrix data as, for example, a referencematrix 310. An operation 616 may be executed that selects certainpatterns. This operation 616 may, in some example embodiments, receiveinput from, for example, the user 101 such the user 101 uses some typeof I/O device and selects one of the patterns displayed within, forexample, the reference matrix 310 so as to generate a selected patternsor pattern 130. An operation 617 may then be executed to transmit forstorage the selected patterns or patterns 130 into the manual patterndatabase 114. In some example embodiments, the operations 601 through603 and 614 through 617 may reside as a part of the one or more devices102. Some example embodiments may also include the operations 614through 613 residing as a part of, for example, the pattern server 112.

FIG. 7 is a flowchart illustrating example method used to executeoperation 706. Shown is a selection instruction set 701. An operation702 may be executed that retrieves a parsing grammar based on a selectedA.I. algorithm from, for example, a data store 704. The retrieval of theparticular parsing grammar may be based upon the instructions containedwithin the selection instruction set 701. An operation 703 may beexecuted that transmits the parsing grammar.

FIG. 8 is a flowchart illustrating example method used to executeoperation 608. Shown is an operation 801 that receives a selectedparsing grammar. An operation 802 may then be executed that parsespattern data 111, extracting certain characteristics of pattern 111 datasuch as, for example, nodes, node values, edges, edge weights, degrees,sink identifiers, source identifiers, and other characteristics of thegraph contained within the pattern data 111. An operation 803 may thenbe executed to generate a pattern characteristic set containing one ormore of the above identified characteristics.

FIG. 9 is a flowchart illustrating an example method used to executeoperation 612. Shown is an operation 901 that when executed receives apattern characteristic set. Also shown is an operation 902 that whenexecuted receives a selection instruction set. An operation 903, whenexecuted, retrieves an A.I. algorithm from the A.I. algorithm database113. An operation 904 may be executed that builds an A.I. data structureusing the A.I. algorithm retrieved from the A.I. algorithm database 113.An operation 905 may be executed that classifies the resulting A.I. datastructure based upon some historical taxonomy. An operation 906 may beexecuted that transmits this A.I. generated dataset.

In some example embodiments, as will be more fully discussed below,historical data is retrieved and used to train the A.I. algorithm(s)retrieved through the execution of the operation 903. Once sufficientlytrained, an A.I. data structure may be implemented to generate secondarynetworks. The various A.I. algorithms that may be implemented are morefully discussed below.

Some example embodiments may include any number of deterministicalgorithms implemented in the A.I. algorithm database 113, includingcase-based reasoning, Bayesian networks (including hidden Markovmodels), neural networks, or fuzzy systems. The Bayesian networks mayinclude: machine learning algorithms including-supervised learning,unsupervised learning, semi-supervised learning, reinforcement learning,transduction, learning to learn algorithms, or some other suitableBayesian network. The neural networks may include: Kohonenself-organizing network, recurrent networks, simple recurrent networks,Hopfield networks, stochastic neural networks, Boltzmann machines,modular neural networks, committee of machines, Associative NeuralNetwork (ASNN), holographic associative memory, instantaneously trainednetworks, spiking neural networks, dynamic neural networks, cascadingneural networks, neuro-fuzzy networks, or some other suitable neuralnetwork. Further, the neural networks may include: machine learningalgorithms including-supervised learning, unsupervised learning,semi-supervised learning, reinforcement learning, transduction, learningto learn algorithms, or some other suitable learning algorithm.

In some embodiments, any number of stochastic algorithms may beimplemented including: genetic algorithms, ant algorithms, tabu searchalgorithms, or Monte Carlo algorithms (e.g., simulated annealing).Common to these algorithms is the use of randomness (e.g., randomlygenerated numbers) to avoid the problem of being unduly wedded to alocal minima or maxima.

FIG. 10 is a flowchart illustrating example method used to executeoperation 613. Shown is an operation 1001 that when executed receives anA.I. generated dataset. A decisional operation 1002 may be executed thatdetermines whether additional A.I. generated datasets are necessary. Incases where decisional operation 1002 evaluates to true, operation 1001is re-executed. In cases where decisional operation 1002 evaluates tofalse, a further operation 1003 is executed that builds a matrix usingan A.I. generated dataset.

FIG. 11 is a flowchart illustrating an example method used to executeoperation 904. Shown is an operation 1101 that receives a patterncharacteristic set. An operation 1102 is executed that selectshistorical data through retrieving this historical data from, forexample, historical data store 120. An operation 1103 may be executedthat combines the selected historical data and pattern characteristicset data based upon some type of crossover and/or mutation function. Adecisional operation 1104 may be executed that determines whether or notthe diversity is established based on the execution of the crossoverand/or mutation function. In cases where decisional operation 1104evaluates to “true,” diversity is established amongst the data containedin the pattern characteristic set. An operation 1105 is then executedthat transmits the resulting A.I. data structure. In cases wheredecisional operation 1104 evaluates to “false,” the operation 1103 isre-executed.

In some example embodiments, diversity may be based upon somepredetermined number of iterations, recursive movements, and sampling.For example, the cross-over and/or mutation functions may be executedand the resulting data structure sampled to determine how it differsfrom some model case of diversity. This model case may be the historicaldata. A termination case may be set based upon the historical data suchthat where the termination case is met diversity is deemed to have beenestablished.

FIG. 12 is a diagram of example graphs 1200 that may be generated as aresult of the execution of mutation function as described in operation1103. Shown is a set 1201 containing member graphs 1202, 1203 and 1204.Each of these graphs has a number of characteristics such that, forexample, the graph 1202 has three nodes and two edges, the graph 1203has two nodes and one edge, and the graph 1204 has one node and oneself-referencing edge (e.g., a cycle). In some example embodiments, agraph 1205 is shown containing a number of nodes such as nodes 1206,1207, 1208, 1209 and 1211. These various nodes are connected viaplurality of edges. In one example embodiment, through the execution ofa mutation function, one of these nodes, here for example node 1209, israndomly selected. Once selected, this node 1209 and, in some cases,associated child nodes are replaced with a graph taken from the set1201. Here for example, the graph 1204 is used to replace any node 1209and its child node 1211, generating a new graph 1215.

FIG. 13 is a diagram of a plurality of graphs that are generated throughthe execution of a crossover function as described in operation 1103.Shown is a set 1301 containing a graph 1202, 1203 and 1204. Further, insome example embodiments, the graph 1205 is used as is the graph 1202for the purposes of executing the crossover function. A node, such asnode 1209, is randomly selected from the graph 1205. Further, a node,such as node 1302, is randomly selected from the graph 1202. Further,this graph 1202 also contains a node 1301 and node 1303. Through theexecution of a crossover function, the randomly selected node of onegraph is, and its children, are replaced with the node of a secondrandomly selected graph and its children. Here node 1209 and node 1211are randomly selected and replaced with a node 1302. Similarly, node1302 is randomly selected and replaced with the nodes 1209 and 1211. Theresult of this crossover is that a new graph 1304 is generated and a newgraph 1305 is generated. In certain example cases, set 1301 is composedof members of data contained in the historical data store 120.

In some example embodiments, the operation 904 described in FIG. 9 andthe associated graphs described in FIGS. 12 and 13 are reflective of theimplementation of a genetic algorithm. While a genetic algorithm is anon-deterministic algorithm, certain other types of artificialintelligence algorithms may be utilized that are deterministic innature.

FIG. 14 is a flowchart illustrating an example method used to executeoperation 904. Shown is an operation 1401 that receives a patterncharacteristic set. An operation 1402 may be executed that selectshistorical data from a historical data store 120. An operation 1403 maybe executed that applies some type of learning paradigm to thehistorical data wherein this learning paradigm may be, for example,supervised learning, unsupervised learning, reinforcement learning orsome other suitable type of learning algorithm. An operation 1404 may beexecuted that transmits the A.I. data structure resulting from theapplication of certain A.I. algorithms for display within the GUI 116.

FIG. 15 is a diagram of an example neural network 1500 generated throughthe application of a learning algorithm. Shown is an input layercontaining nodes 1501 and 1502, a hidden layer containing nodes 1503,1504 and 1505, and an output layer containing nodes 1506 and 1507. Insome example embodiments, the input layer receives input in the form ofpattern data 111 which is then processed through the hidden layer andassociated nodes 1503 through 1505. This hidden layer may containcertain types of functions used to facilitate some type of learningalgorithm such as the aforementioned supervised, unsupervised orreinforcement learning algorithms. The output layer nodes 1506 and 1507may then be used to output, for example, various A.I. data structuresthat are reflected in the secondary network data 115.

Some example embodiments may include training nodes 1503 through 1505with historical data retrieved from the historical data store 120. Thistraining may include instructing the nodes 1503 through 1505 as to whatdata to look for, and what type of data to exclude during the course ofprocessing the pattern data. For example, pattern data describing a node(e.g., nodes in a graph) with more than six degrees may be excludedbased of the training of the nodes 1503 through 1505 using historicaldata showing that nodes of more than three degrees are neverencountered.

In some example embodiments, a neural network utilizing one or more ofthese learning algorithms may be implemented. In other exampleembodiments, a neural network may be implemented utilizing certain typesof non-deterministic algorithms such as a Monte Carlo algorithm or someother suitable algorithm. Description of deterministic andnon-deterministic algorithms is provided below. As previouslyreferenced, these various deterministic and non-deterministic algorithmsmay be utilized independently or in conjunction with one another forpurposes of generating the secondary network data 115.

FIG. 16 is a flowchart illustrating an example method used to executeoperation 616. Shown is an operation 1601 that receives parsed matrixdata. An operation 1602 may be executed that retrieves filteringinstructions from, for example, filtering data 1603. These filteringinstructions may instruct a further operation 1604 to filter out variousedges between various nodes contained in any one of a number of graphsdisplayed in, for example, the reference matrix 310. An operation 1604may be executed that filters the parsed matrix data to reveal relationsin the form of edges connecting certain nodes.

FIG. 17 is a flowchart illustrating an example method used to executeoperation 617. Shown is an operation 1701 that receives informationrelating to the selected pattern. An operation 1702 may be executed thatparses the selected pattern into constituent parts wherein theseconstituent parts may be, for example, edge information, degreeinformation, node information, or other information indicative ofdescribing various attributes associated with a graph. An operation 1703may be executed that formats and transmits the selected parsed pattern.

Example Storage

Some embodiments may include the various databases (e.g., 109, 110, 113,114, and/or 120) being relational databases, or in some cases On-LineAnalytical Processing (OLAP) based databases. In the case of relationaldatabases, various tables of data are created and data is inserted into,and/or selected from, these tables using SQL, or some otherdatabase-query language known in the art. In the case of OLAP databases,one or more multi-dimensional cubes or hypercubes containingmultidimensional data from which data is selected from or inserted intousing MDX may be implemented. In the case of a database using tables andSQL, a database application such as, for example, MYSQL™, SQLSERVER™,Oracle 8I™, 10G™, or some other suitable database application may beused to manage the data. In the case of a database using cubes and MDX,a database using Multidimensional On Line Analytic Processing (MOLAP),Relational On Line Analytic Processing (ROLAP), Hybrid Online AnalyticProcessing (HOLAP), or some other suitable database application may beused to manage the data. These tables or cubes made up of tables, in thecase of, for example, ROLAP, are organized into a RDS or ObjectRelational Data Schema (ORDS), as is known in the art. These schemas maybe normalized using certain normalization algorithms so as to avoidabnormalities such as non-additive joins and other problems.Additionally, these normalization algorithms may include Boyce-CoddNormal Form or some other normalization, optimization algorithm known inthe art.

FIG. 18 is a Relational Data Schema (RDS) 1800. Contained as a part ofthis RDS 1800 may be any one of a number of tables. For example, table1801 contains various deterministic algorithms where these deterministicalgorithms may be, for example, a Baysian network, or some type ofsupervised or unsupervised or machine learning type algorithm. Thesedeterministic algorithms may be stored utilizing some type of, forexample, Binary Large Object (BLOB) formatted data, XML, or some othertype of formatting regime. Table 1802 contains variousnon-deterministic, or stochastic, algorithms where thesenon-deterministic algorithms may be, for example, a genetic algorithm, aMonte Carlo algorithm, an ant algorithm, or some other type of algorithmthat uses randomization in its processing and its execution. As withtable 1801, these various non-deterministic algorithms may be formattedusing, for example, a BLOB data type, an XML data type, or some othersuitable data type. Table 1803 is also shown containing various patterndata wherein this pattern data may be formatted using, for example, XMLand may describe, for example, a pattern in the form of a graph whereinthis graph is composed of nodes and edges. Table 1804 is also showncontaining various manual patterns where these manual patterns arepatterns that are selected by, for example, a user 101 and may describe,for example, nodes and edges between nodes. Further, table 1805 is shownthat contains historical data wherein this historical data may be, forexample, a taxonomy of various graphs that may be used, for example, totrain a neural network such as the neural network described in FIG. 14,or may be used to train some other type of deterministic ornon-deterministic algorithm that may be stored in, for example, thetables 1801 or 1802. Further, a table 1806 is provided that containsunique node identifier values, wherein these unique node identifiervalues may be some type of integer value that is used to uniquelyidentify a node and/or, in some cases, a graph composed of nodes andedges. In some cases, these nodes as referenced elsewhere may relate toaccounts, whereas the edges may relate to transactions or therelationships between accounts where these accounts may be senderaccounts or receiver accounts or some other suitable type of accounts.

A Three-Tier Architecture

In some embodiments, a method is illustrated as implemented in adistributed or non-distributed software application designed under athree-tier architecture paradigm, whereby the various components ofcomputer code that implement this method may be categorized as belongingto one or more of these three tiers. Some embodiments may include afirst tier as an interface (e.g., an interface tier) that is relativelyfree of application processing. Further, a second tier may be a logictier that performs application processing in the form oflogical/mathematical manipulations of data inputted through theinterface level, and communicates the results of theselogical/mathematical manipulations to the interface tier, and/or to abackend, or storage tier. These logical/mathematical manipulations mayrelate to certain business rules, or processes that govern the softwareapplication as a whole. A third tier, a storage tier, may be apersistent storage medium or a non-persistent storage medium. In somecases, one or more of these tiers may be collapsed into another,resulting in a two-tier architecture, or even a one-tier architecture.For example, the interface and logic tiers may be consolidated, or thelogic and storage tiers may be consolidated, as in the case of asoftware application with an embedded database. This three-tierarchitecture may be implemented using one technology, or, as will bediscussed below, a variety of technologies. This three-tierarchitecture, and the technologies through which it is implemented, maybe executed on two or more computer systems organized in aserver-client, peer to peer, or so some other suitable configuration.Further, these three tiers may be distributed between more than onecomputer system as various software components.

Component Design

Some example embodiments may include the above illustrated tiers, andprocesses or operations that make them up, as being written as one ormore software components. Common to many of these components is theability to generate, use, and manipulate data. These components, and thefunctionality associated with each, may be used by client, server, orpeer computer systems. These various components may be implemented by acomputer system on an as-needed basis. These components may be writtenin an object-oriented computer language such that a component-orientedor object-oriented programming technique can be implemented using aVisual Component Library (VCL), Component Library for Cross Platform(CLX), Java Beans (JB), Enterprise Java Beans (EJB), Component ObjectModel (COM), Distributed Component Object Model (DCOM), or othersuitable technique. These components may be linked to other componentsvia various Application Programming interfaces (APIs), and then compiledinto one complete server, client, and/or peer software application.Further, these APIs may be able to communicate through variousdistributed programming protocols as distributed computing components.

Distributed Computing Components and Protocols

Some example embodiments may include remote procedure calls being usedto implement one or more of the above illustrated components across adistributed programming environment as distributed computing components.For example, an interface component (e.g., an interface tier) may resideon a first computer system that is remotely located from a secondcomputer system containing a logic component (e.g., a logic tier). Thesefirst and second computer systems may be configured in a server-client,peer-to-peer, or some other suitable configuration. These variouscomponents may be written using the above illustrated object-orientedprogramming techniques, and can be written in the same programminglanguage, or a different programming language. Various protocols may beimplemented to enable these various components to communicate regardlessof the programming language used to write these components. For example,a component written in C++ may be able to communicate with anothercomponent written in the Java programming language through utilizing adistributed computing protocol such as a Common Object Request BrokerArchitecture (CORBA), a Simple Object Access Protocol (SOAP), or someother suitable protocol. Some embodiments may include the use of one ormore of these protocols with the various protocols outlined in the OSImodel or TCP/IP protocol stack model for defining the protocols used bya network to transmit data.

A System of Transmission Between a Server and Client

Some embodiments may utilize the OSI model or TCP/IP protocol stackmodel for defining the protocols used by a network to transmit data. Inapplying these models, a system of data transmission between a serverand client, or between peer computer systems, is illustrated as a seriesof roughly five layers comprising: an application layer, a transportlayer, a network layer, a data link layer, and a physical layer. In thecase of software having a three tier architecture, the various tiers(e.g., the interface, logic, and storage tiers) reside on theapplication layer of the TCP/IP protocol stack. In an exampleimplementation using the TCP/IP protocol stack model, data from anapplication residing at the application layer is loaded into the dataload field of a TCP segment residing at the transport layer. This TCPsegment also contains port information for a recipient softwareapplication residing remotely. This TCP segment is loaded into the dataload field of an IP datagram residing at the network layer. Next, thisIP datagram is loaded into a frame residing at the data link layer. Thisframe is then encoded at the physical layer, and the data transmittedover a network such as an internet, Local Area Network (LAN), Wide AreaNetwork (WAN), or some other suitable network. In some cases, internetrefers to a network of networks. These networks may use a variety ofprotocols for the exchange of data, including the aforementioned TCP/IP,and additionally ATM, SNA, SDI, or some other suitable protocol. Thesenetworks may be organized within a variety of topologies (e.g., a startopology), or structures.

A Computer System

FIG. 19 shows a diagrammatic representation of a machine in the exampleform of a computer system 1900 that executes a set of instructions toperform any one or more of the methodologies discussed herein. One ofthe devices 102 may configured as a computer system 1900. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment or as a peer machinein a peer-to-peer (or distributed) network environment. The machine maybe a High-performance computing (HPC) cluster, a vector based computer,a Beowulf cluster, or some type of suitable parallel computing cluster.In some example embodiments, the machine may be a PC. Further, whileonly a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein. Example embodiments can alsobe practiced in distributed system environments where local and remotecomputer systems, which are linked (e.g., either by hardwired, wireless,or a combination of hardwired and wireless connections) through anetwork, both perform tasks such as those illustrated in the abovedescription.

The example computer system 1900 includes a processor 1902 (e.g., aCentral Processing Unit (CPU), a Graphics Processing Unit (GPU) orboth), a main memory 1901, and a static memory 1906, which communicatewith each other via a bus 1908. The computer system 1900 may furtherinclude a video display unit 1910 (e.g., a Liquid Crystal Display (LCD)or a Cathode Ray Tube (CRT)). The computer system 1900 also includes analphanumeric input device 1917 (e.g., a keyboard), a GUI cursor control1956 (e.g., a mouse), a disk drive unit 1971, a signal generation device1999 (e.g., a speaker) and a network interface device (e.g., atransmitter) 1920.

The drive unit 1971 includes a machine-readable medium 1922 on which isstored one or more sets of instructions 1921 and data structures (e.g.,software) embodying or used by any one or more of the methodologies orfunctions illustrated herein. The software may also reside, completelyor at least partially, within the main memory 1901 and/or within theprocessor 1902 during execution thereof by the computer system 1900, themain memory 1901 and the processor 1902 also constitutingmachine-readable media.

The instructions 1921 may further be transmitted or received over anetwork 1926 via the network interface device 1920 using any one of anumber of well-known transfer protocols (e.g., Hyper Text TransferProtocol (HTTP), Session Initiation Protocol (SIP)).

The term “machine-readable medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “machine-readable medium” shall also be taken toinclude any medium that is capable of storing, encoding, or carrying aset of instructions for execution by the machine and that cause themachine to perform any of the one or more of the methodologiesillustrated herein. The term “machine-readable medium” shall accordinglybe taken to include, but not be limited to, solid-state memories,optical and magnetic medium, and carrier wave signals.

Marketplace Applications

In some example embodiments, a system and method is shown to facilitatenetwork analysis. The network may be, for example, a fraud network,marketing network, or some other suitable network used to describetransactions between person in commerce. Analysis may, in some exampleembodiments, be performed via manual inspection by a user who compares aprimary network to one or more secondary networks. Once this comparisonis performed, the user may classify the primary network as being part ofa classification used to describe the secondary network. In some exampleembodiments, the primary network may then be used as a secondary networkin later classifications performed by the user.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b), requiring an abstract that may allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it may not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment.

1. A method comprising: receiving pattern data of a primary network that includes transaction data relating to transactions between entities; building, using at least one processor, at least one secondary network based on the pattern data of the primary network, the building including using an Artificial Intelligence (AI) algorithm that processes a pattern characteristic set by combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and graphically displaying, using nodes and edges, the primary network and the at least one secondary network.
 2. The method of claim 1, wherein the building comprises parsing the pattern data using a retrieved parsing grammar to generate the pattern characteristic set.
 3. The method of claim 1, wherein the building comprises building an AI data structure using the AI algorithm, historical data; and the pattern characteristic set.
 4. The method of claim 3, further comprising classifying the AI data structure based on a historical taxonomy.
 5. The method of claim 1, wherein the building comprises applying a learning paradigm to historical data, the learning paradigm being a selection from the group consisting of supervised learning, unsupervised learning, and reinforcement learning.
 6. The method of claim 1, further comprising storing the at least one secondary network into a data store as a manual pattern, the manual pattern being a stored pattern that closely corresponds to a pattern of the pattern data.
 7. The method of claim 1, wherein a transaction data comprises at least one of sales transaction data or a payment transaction data.
 8. A system comprising: a processor of a machine; a receiver to receive pattern data of a primary network that includes transaction data relating to transactions between entities; a building engine to build, using the processor of the machine, at least one secondary network based upon the pattern data of the primary network, the building, engine to build by using an Artificial Intelligence (AI) algorithm that processes a pattern characteristic set by combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and a display component to graphically display, using nodes and edges, the pattern data of the primary network and the at least one secondary network.
 9. A non-transitory machine-readable medium comprising instructions, which when implemented by one or more processors of a machine, cause the machine to perform operations comprising: receiving pattern data of a primary network that includes transaction data relating to transactions between entities; building at least one secondary network based on the pattern data of the primary network, the building including using an Artificial Intelligence (AI) algorithm that processes a pattern characteristic set by combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and generating a graphical display, using nodes and edges, of the pattern data of the primary network and the at least one secondary network.
 10. The non-transitory machine-readable medium of claim 9, wherein the building comprises parsing the pattern data using a retrieved parsing grammar to generate the pattern characteristic set.
 11. The non-transitory machine-readable medium of claim 9, wherein the building comprises building an AI data structure using the AI algorithm, historical data, and the pattern characteristic set.
 12. The non-transitory machine-readable medium of claim 11, wherein the operation further comprise classifying the AI data structure based on a historical taxonomy.
 13. The non-transitory machine-readable medium of claim 9, wherein the building comprises applying a learning paradigm to historical data, the learning paradigm being a selection from the group consisting of supervised learning, unsupervised learning, and reinforcement learning.
 14. The non-transitory machine-readable medium of claim 9, wherein the operations further comprise storing the at least one secondary network into a data store as a manual pattern, the manual pattern being a stored pattern that closely corresponds to a pattern of the pattern data.
 15. The non-transitory machine-readable medium of claim 9, wherein a transaction data comprises at least one of sales transaction data or a payment transaction data.
 16. The method of claim 1, further comprising: determining whether diversity is established based on execution of the crossover or mutation function; and re-executing the combining of the selected historical data and the pattern characteristic set in response to a determination that diversity is not established.
 17. The system of claim 8, wherein the building engine is further to: determine whether diversity is established based on execution of the crossover or mutation function, and re-execute the combining of the selected historical data and the pattern characteristic set in response to a determination that diversity is not established.
 18. The non-transitory machine-readable medium if claim 9, wherein the operations further comprise: determining whether diversity is established based on execution of the crossover or mutation function; and re-executing the combining of the selected historical data and the pattern characteristic set in response to a determination that diversity is not established. 